Publication in fiscal year 2017 (Apr. 2017 – Mar. 2018)

Journal Paper

  1. M. Ogura, M. Wakaiki, H. Rubin, and V. M. Preciado,
    Delayed bet-hedging resilience strategies under environmental fluctuations,”
    Physical Review E, Vol. 95, No. 5, p. 052404 (2017)
  2. Yunduan Cui, Takamitsu Matsubara, and Kenji Sugimoto,
    Pneumatic artificial muscle-driven robot control using local update reinforcement learning“,
    Advanced Robotics, Vol.31, No. 8, pp. 397–412 (2017)
  3. M. Ogura and V. M. Preciado,
    Optimal design of switched networks of positive linear systems via geometric programming,”
    IEEE Transactions on Control of Network Systems, Vol. 4, No. 2, pp. 213–222 (2017)
  4. Yunduan Cui, Takamitsu Matsubara, and Kenji Sugimoto,
    Kernel dynamic policy programming: Applicable reinforcement learning to robot systems with high dimensional states“,
    Neural Networks, Vol. 94, pp. 13–23 (2017)
  5. M. Ogura, A. Cetinkaya, T. Hayakawa, and V. M. Preciado,
    State feedback control of Markov jump linear systems with hidden-Markov mode observation,”
    Automatica (accepted).

International Conference

  1. Y. Koishihara, S. Arnold, K. Yamazaki and T. Matsubara,
    “Hanging Work of T-shirt in Consideration of Deformability and Strechability”,
    2017 IEEE International Conference on Information and Automation (ICIA), pp. ??-??, Macau, China 2017.
  2. M. Ogura, V. M. Preciado,
    “Katz centrality of Markovian temporal networks: analysis and optimization”,
    2017 American Control Conference, pp. 5001-5006, Seattle, USA, 2017.
  3. M. Wakaiki, M. Ogura, J. P. Hespanha,
    Linear quadratic control for sampled-data systems with stochastic delays“,
    2017 American Control Conference, pp. 1978-1983, Seattle, USA, 2017.
  4. James Poon, Yunduan Cui, Jaime Valls Miro, Takamitsu Matsubara, Kenji Sugimoto
    “Local Driving Assistance from Demonstration for Mobility Aids”,
    2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 5935-5941, Singapore, 2017.
  5. V. M. Preciado, M. Ogura,
    “Optimally containing epidemic processes on temporal and adaptive networks”,
    NetSci 2017, Indianapolis, 2017.
  6. Kenji Sugimoto, Wataru Imahayashi,
    “Direct Tuning in Feedback Error Learning Control and Its Generalization to Non-Minimum Phase Plant”,
    20th IFAC World Congress, TuM16.3, Toulouse, 2017.7.9-14 (2017.7.11).
  7. W. Imahayashi, K. Sugimoto,
    “Tolerance to Temporal Sensing Failure in Feedforward Learning Control “,
    SICE Annual Conference 2017, pp. 668–673, Kanazawa, 2017.9.19-22 (2017.9.20).
  8. Y. Iwai, Y. Minami, K. Sugimoto,
    “Prediction Governor for Nonlinear Affine Systems and Its Application to Automatic Cruise Control”,
    SICE Annual Conference 2017, pp. 1336–1339, Kanazawa, 2017.9.19-22 (2017.9.22).
  9. Yoshihisa Tsurumine, Yunduan Cui, Eiji Uchibe, Takamitsu Matsubara,
    “Deep Dynamic Policy Programming for Robot Control with Raw Images”,
    2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1545-1550, Vancouver, 2017.9.24-28 (2017.9.25).
  10. X. Chen, M. Ogura, K. R. Ghusinga, A. Singh, and V. M. Preciado, “Semidefinite bounds for moment dynamics: application to epidemics on networks,”
    56th IEEE Conference on Decision and Control, pp. 1-8, Melbourne, 2017.

Book Chapters

  1. M. Ogura and V. M. Preciado,
    Optimal containment of epidemics in temporal and adaptive networks,”
    in Temporal Networks Epidemiology. Springer-Verlag, pp. 241-266, in press (2017).
  2. V. M. Preciado, M. Zargham, C. Nowzari, S. Han, M. Ogura, A. Jadbabaie, and G. J. Pappas,
    “Bio-inspired framework for allocation of protection resources in cyber-physical networks,”
    in Principles of Cyber-Physical Systems, Cambridge University Press, in press (2017).